Companies today use analytics to provide offers, etc. that target a specific audience or individual. Analytics may be described as using statistics to solve problems in business. Analytics may also be described as analyzing data (using mathematics/statistics) to predict what offers, etc. should be provided to a particular target. Thus, analytics provides recommendations based on insights derived through the application of statistical models and analysis against data.
Existing systems for analytics are tightly coupled with backend systems, such as data warehouses, for data and learning dependency. Therefore, solution development, integration and deployment may be slow and expensive. A data warehouse may be described as a database used for reporting and analysis.
FIG. 1 illustrates a prior art offline analytics environment 100. In FIG. 1, three transaction channels provide input to a transaction management system. A transaction channel may be described as a pathway for a transaction to reach the transaction management system. For example, a bank may have a transaction channel through which banking transactions are routed to the transaction management system. The transaction management system saves transactions in a transaction database and in a data warehouse. The data warehouse may be realized using a database. The analytics processes the transactions in the data warehouse to provide offers, etc. to be used in marketing campaigns.
FIG. 2 illustrates a prior art inline analytics environment 200. In FIG. 2, three transaction channels provide input to a transaction management system. The transaction management system includes recommendation rules (both static and predictive rules) that are saved in a rules database. Static rules may be described as rules that are pre-defined by business users/humans, and predictive rules may be described as rules that are automatically generated (i.e., learnt or discovered) from data. The transaction management system saves transactions in the transaction database and the data warehouse. The analytics processes the transactions in the data warehouse using the rules to provide offers etc. to be used in marketing campaigns
The analytics may depend on confidential data (e.g., banking transactions). Also, the rules and recommendations may be at a segment-level (i.e., directed towards a segment of the population).